Displaying 19 resources
Other Other

Shedding light on AI bias with real world examples

Opinion piece by IBM on bias and AI.
Category
Understanding of the fundamental rights and values
Target audience
Individual Citizens/Members of the Society, Private Sector, Public Sector
Source
Adra-e
Article/Books/eBooks Article/Books/eBooks

Generative AI and Research Integrity

The article critically reviews the use of generative AI is research.
Category
Support guidance in the responsible implementation of ADR, Understanding of the fundamental rights and values
Target audience
Researchers and Academic
Source
Adra-e
Article/Books/eBooks Article/Books/eBooks

Cooperating with machines

Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go).
Category
Understanding of the fundamental rights and values
Target audience
ADR Experts and Associations, Policy Makers, Researchers and Academic
Source
Adra-e
Article/Books/eBooks Article/Books/eBooks

Should we fear the robot revolution? (The correct answer is yes)

Advances in artificial intelligence and robotics may be leading to a new industrial revolution. This paper presents a model with the minimum necessary features to analyze the implications for inequality and output.
Category
Understanding of the fundamental rights and values
Target audience
ADR Experts and Associations, Individual Citizens/Members of the Society, Policy Makers, Private Sector, Public Sector, Researchers and Academic
Source
Adra-e
Software resources Software resources

Decentralized-gnn

A package for implementing and simulating decentralized Graph Neural Network algorithms for classification of peer-to-peer nodes. Developed code supports the publication p2pGNN: A Decentralized Graph Neural Network for Node Classification i
Category
System architectures, Technology methodologies and landscape
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e
Software resources Software resources

PandA: Unsupervised learning of parts and appearances in the feature maps of GANs

We propose an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion.
Category
System architectures
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e